Exploring university students' sports tourism behavior: Based on Structural Equation Model
Bibliographic record
Abstract
China can be considered one of the countries that have maintained the longest duration of epidemic prevention and control policies. The shift in epidemic prevention and control policies presents both opportunities and challenges for the development of sports tourism in China. Compared to other forms of tourism, sports tourism has a certain threshold for athletic skills and may entail certain risks. In recent years, the rapid development of webcasting on the internet has become one of the decisive factors for young people in their travel choices. To promote the healthy and sustainable development of sports tourism after the transition of epidemic prevention and control policies, this study applied the S-O-R theory and planned behavior theory and incorporated new variables such as risk perception and webcast environment to examine the participation behavior of university students in sports tourism. A questionnaire survey was conducted among university students from five universities in central China to collect data. The structural equation model and conditional process model were used to evaluate the research model. Results show that university students have a "willing but hesitant" tendency towards sports tourism, with high intention but low actual participation behavior. The influencing mechanism of university students' participation in sports tourism is complex, with subjective norms as dominant factors, perceived behavioral control as an auxiliary factor, and risk perception as an inhibiting factor. The webcast environment has a significant moderating effect on the relationship between risk perception, participation attitude, behavioral intention, and actual behavior in sports tourism, with a regulatory effect on consumer participation attitudes. Based on the above results, corresponding strategies and suggestions are put forward for the sustainable and healthy development of sports tourism in China.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".